气候模式中关键陆面植被参量遥感估算的研究进展简

Remote Sensing Estimates of Key Land Surface Vegetation Variables Used in Climate Model: A Review

Satellite remote sensing data play an important role in the improvement of climate models forcing field, relevant physical parameters and simulation accuracy. At present, there are many years of satellite remote sensing data and a variety of products about land surface attributes. However, the application of satellite remote sensing data to climate models is still very limited. Fully using satellite remote sensing data is important to improving the simulation ability. In the paper, remote sensing estimates methods of three key land surface parameters including Fractional Vegetation Coverage（FVC）, Leaf Area Index（LAI）and surface albedo（Albedo）is reviewed and up or down scaling land surface variables in validation process is analyzed. Secondly, taking WRF（Weather Research and Forecasting）model as an example, three parameters in climate model are described. Finally, the key problems of using remote sensing data in climate models are discussed, which comprise the uncertainties and scales of remote sensing estimation parameters and the future direction is prospected.